Data in results_gtemc_1.csv
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Variable	Description
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QPRD	=	production
QCON	=	consuption
QIWS	=	Trade
ps	=	price paid to producer (note this is different from variable PM = market price)
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GTEM-C shows resutls as % change year by year.

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Scenario		Description
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A: BAU		Business as usual. No agricultural shocks, no climate feedbacks. 
B: RCP8.5ag	Agricultural shocks and climate feedbacks to reach CO2 emissions of the RCP 8.5 scenario.
B-A: 		B minus A to assess the cost of climate change due to impact of climate on agriculture.
C: RCP4.5	Climate shocks to reach CO2 emissions trajectory of the RCP 4.5 scenario.
A-C:		A minus C to assess the structural change of the trade due to decarbonising the economy. Cost of transition.
D: RCP4.5ag	Agricultural shocks and climate feedbacks to reach CO2 emissions of the RCP 4.5 scenario.
A-D:		A minus D to assess the cost of climate and agricultural impacts combined.
B-D:		B minus D to assess the cost of transitioning and decarbonising.
E: Technology	Simulation ran with CO2 emission trajectory of RCP8.5 and agricultural shocks from RCP 4.5. 
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Data in results_gtemc_2.csv
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Variable	Description
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"Year"    
"svd.SimB"	S index for scenario B
"svd.SimD"	S index for scenario D
"svd.SimC"	S index for scenario C
"svd.SimA"	S index for scenario A
"svdDC"		S index for scenario D minus C
"svdBA"   	S index for scenario B minus A
"simB_25"	25th perceltile of the S index from scenario B
"simB_75" 	75th perceltile of the S index from scenario B
"simD_25"	25th perceltile of the S index from scenario D
"simD_75" 	75th perceltile of the S index from scenario D
"svdDC_75"	75th perceltile of the S index from scenario C minus D
"svdDC_25"	25th perceltile of the S index from scenario C minus D
"svdBA_75"	75th perceltile of the S index from scenario B minus A
"svdBA_25"	25th perceltile of the S index from scenario B minus A
"svdAC"		S index for scenario A minus C
"svdBD"		S index for scenario B minus D
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References for the AgMIP data:
We obtained the AgMIP data from: https://mygeohub.org/resources/agmip
NetCDF (maize) file with grid-cell level yields generated by the LPJmL crop model using climate data from the GFDL-ESM2M GCM under representative concentration pathway RCP 8.5 (scenario SSP2) without irrigation and with CO2 fertilization as documented in Rosenzweig et al. (2014). Data and modeling protocols are described in Elliott (2014). 


Elliott, J., C. Mueller, D. Deryng, J. Chryssanthacopoulos, K. J. Boote, M. Buechner, I. Foster, et al. 2014. "The Global Gridded Crop Model Intercomparison: Data and Modeling Protocols for Phase 1 (v1.0)." Geosci. Model Dev. Discuss. 7 (4): 4383-4427.
Rosenzweig, C., J. Elliott, D. Deryng, A.C. Ruane, C. Mueller, A. Arneth, K.J. Boote, C. Folberth, M. Glotter, N. Khabarov, K. Neumann, F. Piontek, T.A.M. Pugh, E. Schmid, E. Stehfest, H. Yang, and J.W. Jones. 2014. "Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison." Proceedings of the National Academy of Sciences 111:3268-3273.
For definitions, descriptions, and limitations of these data please refer to Rosenzweig et al (2014) PNAS 111(9): 3268-3273.
http://www.pnas.org/content/111/9/3268.full
Different applications of these data can be found at:
Rosenzweig, C. et al. (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111 (9): 3268-3273.
Elliott, J. et al. (2014). Constraints and potentials of future irrigation water availability on global agricultural production under climate change. Proceedings of the National Academy of Sciences, 111 (9): 3239-3244.
Mueller, Christoph, and Richard D. Robertson. "Projecting future crop productivity for global economic modeling." Agricultural Economics 45.1 (2014): 37-50.
Nelson, J. et al. (2014). Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences, 111 (9):  3274-3279.

Links to the models used in the Phase I of the Global Gridded Crop Model Intercomparison (GGCMI) project:
Crop Models
EPIC         http://epicapex.tamu.edu/epic/
GEPIC        http://www.envirogrids.net/Materials/GEPIC/Workshop/1-Introduction/index.html
pDSSAT       https://rdcep.org/research/pdssat-productivity-and-climate-impact-models-0
LPJmL        https://www.pik-potsdam.de/research/projects/activities/biosphere-water-modelling/lpjml
IMAGE-LEITAP http://www.pbl.nl/en/publications/2006/Integratedmodellingofglobalenvironmentalchange.AnoverviewofIMAGE2.4
PEGASUS      http://onlinelibrary.wiley.com/doi/10.1029/2009GB003765/abstract
LPJ-GUESS    http://iis4.nateko.lu.se/lpj-guess/
 
GCMs
HadGEM2-ES     https://verc.enes.org/models/earthsystem-models/metoffice-hadley-centre/hadgem2-es
IPSL-CM5A-LR   https://verc.enes.org/models/earthsystem-models/ipsl/ipslesm
MIROC-ESM-CHEM http://www.geosci-model-dev.net/4/845/2011/gmd-4-845-2011.pdf
GFDL-ESM2M     http://www.gfdl.noaa.gov/earth-system-model
NorESM1-M      http://www.geosci-model-dev.net/6/687/2013/gmd-6-687-2013.html


